Credit Score Data

Dataset contain customer’s credit related information.
Code
# Import Libraries
from nbdev.showdoc import *
import pandas as pd
import numpy as np
from pandas_profiling import ProfileReport
import warnings
warnings.filterwarnings('ignore')

# Load Dataset
df=pd.read_csv('Finance Data/train.csv')

Data Dictionary

There are 28 columns discribing customer information Columns:-

ID: Represents a unique identification of an entry
Customer ID: Represents a unique identification of a person
Month: Represents the month of the year
Name: Represents the name of a person
Age: Represents the age of the person
SSN: Represents the social security number of a person
Occupation: Represents the occupation of the person
Annual_Income: Represents the annual income of the person
Monthly_Inhand_Salary: Represents the monthly base salary of a person
Num_Bank_Accounts: Represents the number of bank accounts a person holds
Num_Credit_Card: Represents the number of other credit cards held by a person
Interest_Rate: Represents the interest rate on credit card
Num_of_Loan: Represents the number of loans taken from the bank
Type_of_Loan: Represents the types of loan taken by a person
Delay_from_due_date: Represents the average number of days delayed from the payment date
Num_of_Delayed_Payment: Represents the average number of payments delayed by a person
Changed_Credit_Limit: Represents the percentage change in credit card limit
Num_Credit_Inquiries: Represents the number of credit card inquiries
Credit_Mix: Represents the classification of the mix of credits
Outstanding_Debt: Represents the remaining debt to be paid (in USD)
Credit_Utilization_Ratio: Represents the utilization ratio of credit card
Credit_History_Age: Represents the age of credit history of the person
Payment_of_Min_Amount: Represents whether only the minimum amount was paid by the person
Total_EMI_per_month: Represents the Equated Monthly Installments payments (in USD)
Amount_invested_monthly: Represents the monthly amount invested by the customer (in USD)
Payment_Behaviour: Represents the payment behavior of the customer (in USD)
Monthly_Balance: Represents the monthly balance amount of the customer (in USD)
Credit_Score: Represents the bracket of credit score (Poor, Standard, Good)

Sample Data

Code
df.head().T
0 1 2 3 4
ID 0x1602 0x1603 0x1604 0x1605 0x1606
Customer_ID CUS_0xd40 CUS_0xd40 CUS_0xd40 CUS_0xd40 CUS_0xd40
Month January February March April May
Name Aaron Maashoh Aaron Maashoh Aaron Maashoh Aaron Maashoh Aaron Maashoh
Age 23 23 -500 23 23
SSN 821-00-0265 821-00-0265 821-00-0265 821-00-0265 821-00-0265
Occupation Scientist Scientist Scientist Scientist Scientist
Annual_Income 19114.12 19114.12 19114.12 19114.12 19114.12
Monthly_Inhand_Salary 1824.843333 NaN NaN NaN 1824.843333
Num_Bank_Accounts 3 3 3 3 3
Num_Credit_Card 4 4 4 4 4
Interest_Rate 3 3 3 3 3
Num_of_Loan 4 4 4 4 4
Type_of_Loan Auto Loan, Credit-Builder Loan, Personal Loan,... Auto Loan, Credit-Builder Loan, Personal Loan,... Auto Loan, Credit-Builder Loan, Personal Loan,... Auto Loan, Credit-Builder Loan, Personal Loan,... Auto Loan, Credit-Builder Loan, Personal Loan,...
Delay_from_due_date 3 -1 3 5 6
Num_of_Delayed_Payment 7 NaN 7 4 NaN
Changed_Credit_Limit 11.27 11.27 _ 6.27 11.27
Num_Credit_Inquiries 4.0 4.0 4.0 4.0 4.0
Credit_Mix _ Good Good Good Good
Outstanding_Debt 809.98 809.98 809.98 809.98 809.98
Credit_Utilization_Ratio 26.82262 31.94496 28.609352 31.377862 24.797347
Credit_History_Age 22 Years and 1 Months NaN 22 Years and 3 Months 22 Years and 4 Months 22 Years and 5 Months
Payment_of_Min_Amount No No No No No
Total_EMI_per_month 49.574949 49.574949 49.574949 49.574949 49.574949
Amount_invested_monthly 80.41529543900253 118.28022162236736 81.699521264648 199.4580743910713 41.420153086217326
Payment_Behaviour High_spent_Small_value_payments Low_spent_Large_value_payments Low_spent_Medium_value_payments Low_spent_Small_value_payments High_spent_Medium_value_payments
Monthly_Balance 312.49408867943663 284.62916249607184 331.2098628537912 223.45130972736786 341.48923103222177
Credit_Score Good Good Good Good Good
Code
profile = ProfileReport(df)
Code
profile.to_notebook_iframe()